File size: 4,471 Bytes
0fc49fc
 
 
 
 
 
 
 
 
 
 
 
 
8a84282
 
0fc49fc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8a84282
d7b4794
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
---
pretty_name: OpenPAV-Trajectory
task_categories:
- tabular-regression
- time-series-forecasting
- other
tags:
- autonomous-driving
- transportation
- trajectory
- vehicle-dynamics
- csv
- tabular
- trajectory-modeling
- car-following-modeling
size_categories:
- 1M<n<10M
configs:
- config_name: Argoverse
  data_files:
  - split: train
    path: data/Argoverse/*.csv
- config_name: CATS
  data_files:
  - split: train
    path: data/CATS/*.csv
- config_name: MicroSimACC
  data_files:
  - split: train
    path: data/MicroSimACC/*.csv
- config_name: Ohio
  data_files:
  - split: train
    path: data/Ohio/*.csv
- config_name: OpenACC
  data_files:
  - split: train
    path: data/OpenACC/*.csv
- config_name: Vanderbilt
  data_files:
  - split: train
    path: data/Vanderbilt/*.csv
viewer: true
---

# OpenPAV-Trajectory

## Dataset Description

OpenPAV-Trajectory is a curated collection of longitudinal vehicle-following trajectories for production automated vehicles (PAVs). It is part of the OpenPAV platform, which supports data collection, behavior modeling, and performance evaluation for production automated driving systems.

This release standardizes public trajectory datasets into one common tabular schema centered on two vehicles: a lead vehicle (LV) and a following automated vehicle (FAV).

The dataset is intended for car-following analysis, trajectory modeling, calibration of behavioral models, benchmarking, and simulation-oriented automated driving research.

OpenPAV project page: <https://openpav.github.io/OpenPAV>

## Key Facts

- 12 CSV files from 6 data providers
- approximately 3,537,455 rows in total
- approximately 675 MB of raw CSV data
- unified schema across all files
- stored as provider-specific subsets for straightforward loading on the Hugging Face Hub

## Repository Structure

```text
OpenPAV-Trajectory/
├── README.md
├── Dataset.png
└── data/
    ├── Argoverse/
    ├── CATS/
    ├── MicroSimACC/
    ├── Ohio/
    ├── OpenACC/
    └── Vanderbilt/
```

Each provider directory is exposed as a separate Hugging Face dataset configuration:

- `Argoverse`
- `CATS`
- `MicroSimACC`
- `Ohio`
- `OpenACC`
- `Vanderbilt`

## Load the Dataset

```python
from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/OpenPAV-Trajectory", "OpenACC")
print(dataset["train"])
```

To load a specific CSV manually:

```python
from datasets import load_dataset

dataset = load_dataset(
    "csv",
    data_files="data/OpenACC/step3_ZalaZone.csv",
)
```

## Data Schema

All CSV files follow the same schema.

| Column | Description | Unit |
| --- | --- | --- |
| `Trajectory_ID` | Unique identifier of a longitudinal trajectory | N/A |
| `Time_Index` | Timestamp within a trajectory | s |
| `ID_LV` | Lead vehicle ID | N/A |
| `Type_LV` | Lead vehicle type: automated vehicle = 1, human-driven vehicle = 0 | N/A |
| `Pos_LV` | Lead vehicle position in Frenet coordinates | m |
| `Speed_LV` | Lead vehicle speed | m/s |
| `Acc_LV` | Lead vehicle acceleration | m/s^2 |
| `ID_FAV` | Following automated vehicle ID | N/A |
| `Pos_FAV` | Following automated vehicle position in Frenet coordinates | m |
| `Speed_FAV` | Following automated vehicle speed | m/s |
| `Acc_FAV` | Following automated vehicle acceleration | m/s^2 |
| `Spatial_Gap` | Bumper-to-bumper spacing between LV and FAV | m |
| `Spatial_Headway` | Center-to-center distance between LV and FAV | m |
| `Speed_Diff` | Relative speed defined as `Speed_LV - Speed_FAV` | m/s |

## Source Datasets

This integrated release currently standardizes public data from the following sources:

- Argoverse 2 Motion Forecasting Dataset
- CATS Open Datasets
- Central Ohio ACC Datasets
- MicroSimACC Dataset
- OpenACC Database
- Vanderbilt ACC Dataset

These sources cover multiple cities, road environments, and automated driving scenarios. The current repository contains transformed and harmonized trajectory tables derived from those public resources.

<img src="./dataset.jpg" alt="OpenPAV-Trajectory overview" width="700">



## Contributing Data

We welcome contributions of PAV trajectory datasets.

Please follow these steps:

1. Fork this dataset repository.
2. Upload your dataset following the structure described below.
3. Submit a Pull Request.
4. The maintainers will review and merge the dataset.

## Contributors

- [Hang Zhou](https://catslab.engr.wisc.edu/staff/zhou-hang/), Keke Long , Chengyuan Ma.